The regulation of mitochondrial transcription factor A (Tfam) expression during skeletal muscle cell differentiation
Bibliographic record
Abstract
The ATP demand required for muscle development is accommodated by elevations in mitochondrial biogenesis, through the co-ordinated activities of the nuclear and mitochondrial genomes. The most important transcriptional activator of the mitochondrial genome is mitochondrial transcription factor A (Tfam); however, the regulation of Tfam expression during muscle differentiation is not known. Thus, we measured Tfam mRNA levels, mRNA stability, protein expression and localization and Tfam transcription during the progression of muscle differentiation. Parallel 2-fold increases in Tfam protein and mRNA were observed, corresponding with 2-3-fold increases in mitochondrial content. Transcriptional activity of a 2051 bp promoter increased during this differentiation period and this was accompanied by a 3-fold greater Tfam mRNA stabilization. Interestingly, truncations of the promoter at 1706 bp, 978 bp and 393 bp promoter all exhibited 2-3-fold higher transcriptional activity than the 2051 bp construct, indicating the presence of negative regulatory elements within the distal 350 bp of the promoter. Activation of AMP kinase augmented Tfam transcription within the proximal promoter, suggesting the presence of binding sites for transcription factors that are responsive to cellular energy state. During differentiation, the accumulating Tfam protein was progressively distributed to the mitochondrial matrix where it augmented the expression of mtDNA and COX (cytochrome c oxidase) subunit I, an mtDNA gene product. Our data suggest that, during muscle differentiation, Tfam protein levels are regulated by the availability of Tfam mRNA, which is controlled by both transcription and mRNA stability. Changes in energy state and Tfam localization also affect Tfam expression and action in differentiating myotubes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".